基于加窗互相关函数的微动面波岩溶塌陷探测

宋同; 李欣欣; 张伟; 胡涛; 郑晓慧

doi:10.11932/karst20240415

基于加窗互相关函数的微动面波岩溶塌陷探测

doi: 10.11932/karst20240415

宋同^{1, 2,},
李欣欣^{1, 2, ,},
张伟³,
胡涛¹,
郑晓慧¹

1.
西安石油大学地球科学与工程学院, 陕西西安 710065
2.
西安石油大学陕西省油气成藏地质学重点实验室, 陕西西安 710065
3.
中国地质科学院岩溶地质研究所, 广西桂林 541004

基金项目: 国家自然科学基金项目（42004110）；陕西省自然科学基础研究计划资助项目（2021JQ-589）

详细信息

作者简介:
宋同（1998－），男，硕士研究生，主要从事微动面波成像方法研究。E-mail：songtong2022@163.com

通讯作者:
李欣欣（1989－），男，理学博士，副教授，主要从事地震层析成像方法研究。E-mail：xxli@xsyu.edu.cn。

中图分类号: P315.9
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-03
- 刊出日期: 2024-10-31

Detection of karst collapses through microtremor surface waves based on windowing cross-correlation function

SONG Tong^{1, 2
,},
LI Xinxin^{1, 2
, ,},
ZHANG Wei³,
HU Tao¹,
ZHENG Xiaohui¹

1.
School of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi 710065, China
2.
Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi'an Shiyou University, Xi'an, Shaanxi 710065, China
3.
Institute of Karst Geology, CAGS, Guilin, Guangxi 541004, China

摘要

摘要: 文章利用微动面波成像方法对江西某研究区的岩溶塌陷进行探测：先针对微动信号的互相关函数进行窗函数优化处理，提高数据信噪比，改善微动面波的频散能量谱分辨率；再对研究区各组虚源面波记录进行频散曲线提取和反演处理，获得测线上各点的横波速度结构；最后联合各测点反演结果生成测线下方的横波速度剖面，并结合钻孔资料进行地质解释，成功揭示区内岩溶塌陷的分布位置及深度。结果表明：（1）互相关函数计算时的窗函数选取将影响频散能量谱的分辨率，在微动数据处理时应进行窗函数测试；（2）对互相关函数进行加窗处理和优化，可有效提高微动面波的信噪比和频散能量谱的分辨率，拓宽频散曲线的频带范围，提高反演的准确性；（3）微动面波技术在岩溶塌陷探测中具有较好的应用效果，结合加窗函数优化处理能更准确确定地下危害体的范围。
- 微动 /
- 面波 /
- 互相关函数 /
- 窗函数 /
- 岩溶塌陷
Abstract: The study area is located in the karst development area of Pingxiang in the west of Jiangxi Province, China. The landform of this area is complex, low in the northwest and high in the southeast. The fold action and gravitational sliding action led to the development of faults and extensional sliding nappe structures in the area, accompanied by magmatic intrusion activities, which has formed multi-phase superimposed complex structures. Atmospheric precipitation and groundwater in the upstream limestone areas constitute the main water source in the study area. Meanwhile, karst collapses may cause the formation of holes below the surface, seriously endangering people's life and property. Therefore, finding out the geological situation of the collapse area can provide a reference for the understanding of the geological characteristics and the groundwater system in this area.Microtremor is a kind of persistent weak vibration signal observed on the surface caused by industrial vibrations, traffic noises, tidal currents, atmospheric activities and other activities on the earth. Surface waves are formed by vertical waves and transverse waves interfering on the surface. They have the characteristics of low speed, low frequency and frequency dispersion, which lay a foundation for the detection of underground structure. The method of microtremor surface waves survey utilizes various types of vibrations that continuously exist in nature as signal sources. It extracts the information on seismic surface wave field from microtremor records and uses this type of information for imaging underground media. The steps include microtremor signal acquisition in the study area, data preprocessing, empirical Green function calculation, extraction of surface wave dispersion curves and inversion of velocity structure for transverse waves. Among these steps, the empirical Green function is obtained through the cross-correlation operation of the microtremor signals recorded by two detectors, and calculating the empirical Green function is the key to obtain the surface wave information.This study detects the karst collapses in the study area by using microtremor surface waves. However, the surface wave signals are affected by uneven distribution of natural noise sources and random noises, which may cause the low signal-to-noise ratio of the Green function. Therefore, the direct use of the empirical Green function for subsequent data processing may get the dispersion energy spectrum with low resolution, which is not conducive to the subsequent extraction of high-quality dispersion curves and accurate inversion.Due to the above shortcomings, this study first optimized the window function for the cross-correlation function of microtremor signals to improve the signal-to-noise rate of microtremor data, and to enhance the resolution of energy spectrum of microtremor surface wave dispersion. Then, the extraction of dispersion curves and inversion of the virtual source surface record of each group in the study area were conducted to obtain the transverse wave velocity structure of each point along the measurement line. Finally, the distribution positions and depths of karst collapses in the study area were revealed, according to the the transverse wave velocity section below the measurement line generated by inversion as well as the geological interpretation of drilling data. The results show as follows, (1) The selection of window function in the cross-correlation function calculation will affect the resolution of the dispersion energy spectrum, and the window function should be tested in processing the microtremor data. (2) The processing and optimizing of window function for the cross-correlation function can effectively improve the signal-to-noise rate of microtremor surface wave and the resolution of the dispersion energy spectrum, widen the frequency band range of dispersion curves, and improve the accuracy of the inversion. (3) The method of microtremor surface waves survey is highly applicable to karst collapse detection, and the optimization of the window function can determine the range of underground hazards in a more accurate way.
- microtremor /
- surface wave /
- cross-correlation function /
- window function /
- karst collapse

HTML

图 1 不同时间窗长度对互相关结果和频散能量谱的影响

Figure 1. Influence of window length on cross-correlation results and dispersive energy spectrum

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图 2 原始面波道集记录

Figure 2. Original trace record of surface waves

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图 3 第8道数据及窗口位置

Figure 3. Data and window location in Trace 8

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图 4 互相关函数加窗后的微动面波道集记录

Figure 4. Trace record of microtremor surface waves after the adding of a window to the cross-correlation function

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图 5 互相关函数加窗前后的微动面波频散能量谱对比

Figure 5. Comparison of the dispersion energy spectrum of microtremor surface waves after the adding of a window to the cross-correlation function

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图 6 7~19道反演结果

Figure 6. Inversion results of Trace 7–Trace 19

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图 7 16~41道反演结果

Figure 7. Inversion results of Trace 16–Trace 41

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图 8 研究区二维横波速度结构剖面

Figure 8. Structural profile of two-dimensionnal transverse wave velocity in the study area

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图 9 钻孔岩心柱状图

Figure 9. Histogram of borehole core

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表 1 每组最小反演误差

Table 1. Minimum inversion error of each group

组别 1~13道 2~14道 3~15道 4~16道 5~17道 6~18道 7~19道 8~20道

相对误差/% 0.6372 0.5738 0.4878 0.3740 0.7260 0.4879 0.4335 0.5893

下载: 导出CSV

组别 9~21道 10~35道 11~36道 12~37道 13~38道 14~39道 15~40道 16~41道

相对误差/% 0.6069 0.3773 0.1811 0.5585 0.7147 0.4911 0.5007 0.4958

下载: 导出CSV

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